In modern information infrastructures, diagnosis must be able to assess the status or the extent of the damage of individual components. Traditional one-shot diagnosis is not adequate, but streams of data on component behavior need to be collected and filtered over time as done by some existing heuristics. This paper proposes instead a general framework and a formalism to model such over-time diagnosis scenarios, and to find appropriate solutions. As such, it is very beneficial to system designers to support design choices. Taking advantage of the characteristics of the hidden Markov models formalism, widely used in pattern recognition, the paper proposes a formalization of the diagnosis process, addressing the complete chain constituted by monitored component, deviation detection and state diagnosis. Hidden Markov models are well suited to represent problems where the internal state of a certain entity is not known and can only be inferred from external observations of what this entity emits. Such over-time diagnosis is a first class representative of this category of problems. The accuracy of diagnosis carried out through the proposed formalization is then discussed, as well as how to concretely use it to perform state diagnosis and allow direct comparison of alternative solutions.
Abstract. In this chapter we discuss the susceptibility of critical information infrastructures to computer-borne attacks and faults, mainly due to their largely computerized nature, and to the pervasive interconnection of systems all over the world. We discuss how to overcome these problems and achieve resilience of critical information infrastructures, through adequate architectural constructs. The architecture we propose is generic and may come to be useful as a reference for modern critical information infrastructures. We discuss four main aspects: trusted components which induce prevention; middleware devices that achieve runtime automatic tolerance and protection; trustworthiness monitoring mechanisms detecting and adapting to non-predicted situations; organization-level security policies and access control models capable of securing global information flows.
Background Eosinophilic gastrointestinal disorders (EGID) are characterized by eosinophilic inflammation and are subclassified according to the affected site(s) as eosinophilic esophagitis, eosinophilic gastritis, eosinophilic enteritis and eosinophilic colitis. Clinical presentation includes dyspeptic symptoms, vomiting, abdominal pain, diarrhoea and gastrointestinal bleeding. Peripheral eosinophilia is usually found but is not required for the diagnosis. The treatment is based on dietary elimination therapy, consisting of removal of common food triggers, most frequently cow’s milk in infants. Corticosteroids are used as first line drug therapy in EG if dietary therapy fails to achieve an adequate clinical response or is impractical. Case presentation A four month old infant was admitted for an episode of melena and hematemesis. An esophagogastroduodenoscopy showed haemorrhagic gastritis with ulcerative lesions and fibrin. A significant gastric bleeding was noted after the procedure. The gastric mucosa biopsies showed an eosinophilic infiltration. Conclusions A clinically relevant anaemia is a quite rare complication in infants with eosinophilic gastritis and a biopsy may worsen bleeding, to a potentially severe level of low haemoglobin. In infants with low haemoglobin levels and suspect eosinophilic gastritis a watchful follow up after the biopsy should be considered, as well as the possibility of postponing the biopsy to reduce the bleeding risk.
Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, including gas, water, electricity, and railway. Despite a huge effort from research communities and industries have been made in addressing the dependability of SCADA systems, the diagnosis of SCADA malfunctions is still a challenging issue today. This paper proposes a Simple Event Correlator engine for diagnosis of malfunctions in SCADA systems based on a rule-based event correlation approach. In particular, it is used to detect and filter "relevant" symptoms useful for fault diagnosis in a SCADA infrastructure
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